Comparing Approaches for Query Autocompletion

Abstract

Within a search engine, query auto-completion aims to predict the final query the user wants to enter as they type, with the aim of reducing query entry time and potentially preparing the search results in advance of query submission. There are a large number of approaches to automatically rank candidate queries for the purposes of auto-completion. However, no study exists that compares these approaches on a single dataset. Hence, in this paper, we present a comparison study between current approaches to rank candidate query completions for the user query as it is typed. Using a query-log and document corpus from a commercial medical search engine, we study the performance of 11 candidate query ranking approaches from the literature and analyze where they are effective. We show that the most effective approaches to query auto-completion are largely dependent on the number of characters that the user has typed so far, with the most effective approach differing for short and long prefixes. Moreover, we show that if personalized information is available about the searcher, this additional information can be used to more effectively rank query candidate completions, regardless of the prefix length.